Enhancing Coherence and Diversity in Multi-class Slogan Generation Systems
Pir Noman Ahmad, Yuanchao Liu, Inam Ullah, Mohammad Shabaz
Abstract
Many problems related to natural language processing are solved by neural networks and big data. Researchers have previously focused on single-task supervised goals with limited data management to train slogan classification. A multi-task learning framework is used to learn jointly across several tasks related to generating multi-class slogan types. This study proposes a multi-task model named slogan generative adversarial network systems (Slo-GAN) to enhance coherence and diversity in slogan generation, utilizing generative adversarial networks and recurrent neural networks (RNN). Slo-GAN generates a new text slogan-type corpus, and the training generalization process is improved. We explored active learning (AL) and meta-learning (ML) for dataset labeling efficiency. AL reduced annotations by 10% compared to ML but still needed about 70% of the full dataset for baseline performance. The whole framework of Slo-GAN is supervised and trained together on all of these tasks. The text with the higher reporting score level is filtered by Slo-GAN, and a classification accuracy of 87.2% is achieved. We leveraged relevant datasets to perform a cross-domain experiment, reinforcing our assertions regarding both the distinctiveness of our dataset and the challenges of adapting bilingual dialects to one another.